Combined strategy for tuning sensor-less brushless DC motor using SEPIC converter to reduce torque ripple

Abstract

The brushless DC motor (BLDCM) is widely used in computer numerical control (CNC) machines, aerospace applications and auto industry applications in the field of robotics. But it is still affected by the transmission torque ripple, which mostly depends on the speed and the transient line current at the transmission interval. This manuscript proposes a combined approach for tuning sensor-less brushless DC (BLDC) motors using a single-ended primary-inductor converter (SEPIC). The proposed technique is a combination of Golden Eagle Optimization (GEO) and Radial Basis Function Neural Network (RBFNN), hence it is called GEO-RPFNN. The control of speed and torque is to reduce the torque ripple in the motor. Here, the modified bridgeless single-ended primary-inductor converter is proposed to improve speed and torque control. The proposed method is used to adjust the parameters of proportional integral derivative (PID) controller and to improve the performance of PID controller. Therefore, the GEO–RBFNN technique is proposed to recover the control loop function. The proposed algorithm is explored to control the speed and torque error as BLDC motor. Nevertheless, the output of the proposed approach is subject to the input of speed and torque controllers. The proposed method is executed in MATLAB Simulink site. The performance of the proposed system is compared with existing FA and PSO methods. As per the state of comparison outcomes, the GEO–RBFNN gives better result than the existing techniques which has higher ability to conquer the related issues. The THD in stator current, power factor and torque ripple gives the value using proposed method is 1.26%, 0.9951 and 7.4.